Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

被引：0
|
作者：
Min Feng
Yang Deng
Libo Yang
Qiuyang Jing
Zhang Zhang
Lian Xu
Xiaoxia Wei
Yanyan Zhou
Diwei Wu
Fei Xiang
Yizhe Wang
Ji Bao
Hong Bu
机构：
[1] West China Hospital,Laboratory of Pathology
[2] Sichuan University,Department of Pathology
[3] West China Second University Hospital,Department of Pathology
[4] Sichuan University & key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University),Department of Pathology
[5] Ministry of Education,undefined
[6] West China Hospital,undefined
[7] Sichuan University,undefined
[8] Chengfei Hospital,undefined
[9] Chengdu Knowledge Vision Science and Technology Co.,undefined
[10] Ltd,undefined
来源：
Diagnostic Pathology | / 15卷
关键词：
Convolutional neural network; Whole tissue sections; Breast invasive ductal carcinoma; Automatic recognition; Ki-67 counting;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma
    Feng, Min
    Deng, Yang
    Yang, Libo
    Jing, Qiuyang
    Zhang, Zhang
    Xu, Lian
    Wei, Xiaoxia
    Zhou, Yanyan
    Wu, Diwei
    Xiang, Fei
    Wang, Yizhe
    Bao, Ji
    Bu, Hong
    DIAGNOSTIC PATHOLOGY, 2020, 15 (01)
  • [2] Deep Learning-Based, Fully Automated Analysis of Whole Slide Images Can Detect Invasive Breast Carcinoma and Count Ki-67 Easily and Precisely
    Hida, Akira
    Thagaard, Jeppe
    Watanabe, Takahiro
    Ramsing, Thomas
    Overgaard, Agnete
    Oshiro, Yumi
    Moriya, Takuya
    LABORATORY INVESTIGATION, 2023, 103 (03) : S143 - S144
  • [3] An Automated and Accurate Methodology to Assess Ki-67 Labeling Index of Immunohistochemical Staining Images of Breast Cancer Tissues
    Razavi, Salar
    Khameneh, Fariba D.
    Serteli, Ercan Alp
    Cayir, Sercan
    Cetin, Sukru Burak
    Hatipoglu, Gokhan
    Kamasak, Mustafa
    Ayalti, Samet
    2018 25TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 2018,
  • [4] Recognition of tumor cell by image analysis in oligodendroglioma at Ki-67 staining
    Markiewicz, T.
    Grala, B.
    Warowny, M.
    Osowski, S.
    Kozlowski, W.
    VIRCHOWS ARCHIV, 2009, 455 : 402 - 402
  • [5] Automated evaluation of the Ki-67 LI in whole slide images: the antibody factor
    Slodkowska, J.
    Markiewicz, T.
    Grala, B.
    Pleskacz, K.
    Murawski, P.
    Kozlowski, W.
    VIRCHOWS ARCHIV, 2011, 459 : S313 - S313
  • [6] Analysis of membranous Ki-67 staining in breast cancer and surrounding breast epithelium
    Gábor Cserni
    Virchows Archiv, 2018, 473 : 145 - 153
  • [7] Analysis of membranous Ki-67 staining in breast cancer and surrounding breast epithelium
    Cserni, Gabor
    VIRCHOWS ARCHIV, 2018, 473 (02) : 145 - 153
  • [8] Automated assessment of Ki-67 in breast cancer: the utility of digital image analysis using virtual triple staining and whole slide imaging
    Hida, Akira, I
    Omanovic, Dzenita
    Pedersen, Lars
    Oshiro, Yumi
    Ogura, Takashi
    Nomura, Tsunehisa
    Kurebayashi, Junichi
    Kanomata, Naoki
    Moriya, Takuya
    HISTOPATHOLOGY, 2020, 77 (03) : 471 - 480
  • [9] Automated Ki-67 Quantification of Immunohistochemical Staining Image of Human Nasopharyngeal Carcinoma Xenografts
    Peng Shi
    Jing Zhong
    Jinsheng Hong
    Rongfang Huang
    Kaijun Wang
    Yunbin Chen
    Scientific Reports, 6
  • [10] Automated Ki-67 Quantification of Immunohistochemical Staining Image of Human Nasopharyngeal Carcinoma Xenografts
    Shi, Peng
    Zhong, Jing
    Hong, Jinsheng
    Huang, Rongfang
    Wang, Kaijun
    Chen, Yunbin
    SCIENTIFIC REPORTS, 2016, 6
12345